{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Kerry\Documents\Alchemy\Publications\230915 - framil\Publication\Replication files\framil_chavez_replicationlog.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}19 Sep 2023, 17:07:56

{com}. do "C:\Users\Kerry\AppData\Local\Temp\STD2978_000000.tmp"
{txt}
{com}. global c = "cold_war rightist audience onset num_motives"
{txt}
{com}. 
. /*Table 1. Predictors of Communication Frames Justifying Military Intervention*/
. nbreg strat_frame strategic_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-960.51532}  
Iteration 1:{space 3}log pseudolikelihood = {res:-959.08399}  
Iteration 2:{space 3}log pseudolikelihood = {res:-959.08181}  
Iteration 3:{space 3}log pseudolikelihood = {res:-959.08181}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-457.25207}  
Iteration 1:{space 3}log pseudolikelihood = {res:-451.61507}  
Iteration 2:{space 3}log pseudolikelihood = {res:-451.60188}  
Iteration 3:{space 3}log pseudolikelihood = {res:-451.60188}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-432.09011}  
Iteration 1:{space 3}log pseudolikelihood = {res:-428.36475}  
Iteration 2:{space 3}log pseudolikelihood = {res: -425.0167}  
Iteration 3:{space 3}log pseudolikelihood = {res:-424.98854}  
Iteration 4:{space 3}log pseudolikelihood = {res:-424.98853}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    310.89
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-424.98853{txt}{col 49}Pseudo R2{col 67}= {res}    0.0589

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} strat_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}strategic_o {c |}{col 14}{res}{space 2} .9611189{col 26}{space 2} .1618582{col 37}{space 1}    5.94{col 46}{space 3}0.000{col 54}{space 4} .6438827{col 67}{space 3} 1.278355
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2} .2661465{col 26}{space 2} .2698624{col 37}{space 1}    0.99{col 46}{space 3}0.324{col 54}{space 4} -.262774{col 67}{space 3} .7950671
{txt}{space 4}rightist {c |}{col 14}{res}{space 2}-.0104871{col 26}{space 2} .2958582{col 37}{space 1}   -0.04{col 46}{space 3}0.972{col 54}{space 4}-.5903585{col 67}{space 3} .5693842
{txt}{space 4}audience {c |}{col 14}{res}{space 2}-.1701301{col 26}{space 2} .2011936{col 37}{space 1}   -0.85{col 46}{space 3}0.398{col 54}{space 4}-.5644623{col 67}{space 3} .2242021
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.1168068{col 26}{space 2} .2514543{col 37}{space 1}   -0.46{col 46}{space 3}0.642{col 54}{space 4}-.6096481{col 67}{space 3} .3760345
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .0456023{col 26}{space 2} .1400638{col 37}{space 1}    0.33{col 46}{space 3}0.745{col 54}{space 4}-.2289178{col 67}{space 3} .3201224
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6191344{col 26}{space 2} .2146596{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4} .1984094{col 67}{space 3} 1.039859
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2} .0165573{col 26}{space 2} .1659973{col 54}{space 4}-.3087914{col 67}{space 3} .3419061
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} 1.016695{col 26}{space 2} .1687687{col 54}{space 4}  .734334{col 67}{space 3} 1.407628
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-451.6019{col 38}-424.9885{col 49}     8{col 58} 865.9771{col 69} 889.2783
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg human_frame humanitarian_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -730.2259}  
Iteration 1:{space 3}log pseudolikelihood = {res:-730.22558}  
Iteration 2:{space 3}log pseudolikelihood = {res:-730.22558}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-438.04897}  
Iteration 1:{space 3}log pseudolikelihood = {res:-437.50518}  
Iteration 2:{space 3}log pseudolikelihood = {res:-437.50505}  
Iteration 3:{space 3}log pseudolikelihood = {res:-437.50505}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-428.47532}  
Iteration 1:{space 3}log pseudolikelihood = {res: -427.0306}  
Iteration 2:{space 3}log pseudolikelihood = {res:-426.95735}  
Iteration 3:{space 3}log pseudolikelihood = {res:-426.95726}  
Iteration 4:{space 3}log pseudolikelihood = {res:-426.95726}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     54.30
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-426.95726{txt}{col 49}Pseudo R2{col 67}= {res}    0.0241

{txt}{ralign 80:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}   human_frame{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanitarian_o {c |}{col 16}{res}{space 2}  .360562{col 28}{space 2} .1129989{col 39}{space 1}    3.19{col 48}{space 3}0.001{col 56}{space 4} .1390883{col 69}{space 3} .5820357
{txt}{space 6}cold_war {c |}{col 16}{res}{space 2}-.4240534{col 28}{space 2} .1594952{col 39}{space 1}   -2.66{col 48}{space 3}0.008{col 56}{space 4}-.7366582{col 69}{space 3}-.1114486
{txt}{space 6}rightist {c |}{col 16}{res}{space 2} .1413669{col 28}{space 2} .1662309{col 39}{space 1}    0.85{col 48}{space 3}0.395{col 56}{space 4}-.1844397{col 69}{space 3} .4671735
{txt}{space 6}audience {c |}{col 16}{res}{space 2}-.1376243{col 28}{space 2}  .220472{col 39}{space 1}   -0.62{col 48}{space 3}0.532{col 56}{space 4}-.5697415{col 69}{space 3} .2944929
{txt}{space 9}onset {c |}{col 16}{res}{space 2}-.3760652{col 28}{space 2} .2524763{col 39}{space 1}   -1.49{col 48}{space 3}0.136{col 56}{space 4}-.8709098{col 69}{space 3} .1187793
{txt}{space 3}num_motives {c |}{col 16}{res}{space 2} .1034978{col 28}{space 2} .1483799{col 39}{space 1}    0.70{col 48}{space 3}0.485{col 56}{space 4}-.1873216{col 69}{space 3} .3943171
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}   2.1925{col 28}{space 2} .3794945{col 39}{space 1}    5.78{col 48}{space 3}0.000{col 56}{space 4} 1.448705{col 69}{space 3} 2.936296
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2}-.0414889{col 28}{space 2} .1738188{col 56}{space 4}-.3821674{col 69}{space 3} .2991897
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2}   .95936{col 28}{space 2} .1667548{col 56}{space 4} .6823808{col 69}{space 3} 1.348765
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-437.5051{col 38}-426.9573{col 49}     8{col 58} 869.9145{col 69} 893.2158
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg terror_frame counterterror_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-428.95723}  
Iteration 1:{space 3}log pseudolikelihood = {res:-305.32986}  
Iteration 2:{space 3}log pseudolikelihood = {res:-281.45551}  
Iteration 3:{space 3}log pseudolikelihood = {res:-281.38324}  
Iteration 4:{space 3}log pseudolikelihood = {res:-281.38322}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-233.83667}  
Iteration 1:{space 3}log pseudolikelihood = {res: -167.9739}  
Iteration 2:{space 3}log pseudolikelihood = {res:-164.93381}  
Iteration 3:{space 3}log pseudolikelihood = {res:-164.83702}  
Iteration 4:{space 3}log pseudolikelihood = {res:-164.83698}  
Iteration 5:{space 3}log pseudolikelihood = {res:-164.83698}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-156.63841}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-148.37229}  
Iteration 2:{space 3}log pseudolikelihood = {res:-144.08703}  
Iteration 3:{space 3}log pseudolikelihood = {res:-143.12544}  
Iteration 4:{space 3}log pseudolikelihood = {res: -143.1191}  
Iteration 5:{space 3}log pseudolikelihood = {res: -143.1191}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    300.86
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -143.1191{txt}{col 49}Pseudo R2{col 67}= {res}    0.1318

{txt}{ralign 81:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   terror_frame{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
counterterror_o {c |}{col 17}{res}{space 2}  1.18264{col 29}{space 2}  .342668{col 40}{space 1}    3.45{col 49}{space 3}0.001{col 57}{space 4} .5110235{col 70}{space 3} 1.854257
{txt}{space 7}cold_war {c |}{col 17}{res}{space 2}-.9864667{col 29}{space 2} .4700775{col 40}{space 1}   -2.10{col 49}{space 3}0.036{col 57}{space 4}-1.907802{col 70}{space 3}-.0651317
{txt}{space 7}rightist {c |}{col 17}{res}{space 2} 1.892649{col 29}{space 2}  .548678{col 40}{space 1}    3.45{col 49}{space 3}0.001{col 57}{space 4} .8172595{col 70}{space 3} 2.968038
{txt}{space 7}audience {c |}{col 17}{res}{space 2}-1.516078{col 29}{space 2} .2730966{col 40}{space 1}   -5.55{col 49}{space 3}0.000{col 57}{space 4}-2.051338{col 70}{space 3}-.9808187
{txt}{space 10}onset {c |}{col 17}{res}{space 2}-.5807622{col 29}{space 2} .2712325{col 40}{space 1}   -2.14{col 49}{space 3}0.032{col 57}{space 4}-1.112368{col 70}{space 3}-.0491563
{txt}{space 4}num_motives {c |}{col 17}{res}{space 2} 1.230199{col 29}{space 2} .3412598{col 40}{space 1}    3.60{col 49}{space 3}0.000{col 57}{space 4} .5613422{col 70}{space 3} 1.899056
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} -2.34365{col 29}{space 2} .8555427{col 40}{space 1}   -2.74{col 49}{space 3}0.006{col 57}{space 4}-4.020483{col 70}{space 3}-.6668173
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2} 1.235337{col 29}{space 2} .1802708{col 57}{space 4}  .882013{col 70}{space 3} 1.588661
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 3.439538{col 29}{space 2} .6200481{col 57}{space 4} 2.415758{col 70}{space 3} 4.897189
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27} -164.837{col 38}-143.1191{col 49}     8{col 58} 302.2382{col 69} 325.5394
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg econ_frame economic_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-702.12945}  
Iteration 1:{space 3}log pseudolikelihood = {res:-702.12812}  
Iteration 2:{space 3}log pseudolikelihood = {res:-702.12812}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-353.64912}  
Iteration 1:{space 3}log pseudolikelihood = {res:-327.21439}  
Iteration 2:{space 3}log pseudolikelihood = {res:-327.14462}  
Iteration 3:{space 3}log pseudolikelihood = {res:-327.14461}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-322.60117}  
Iteration 1:{space 3}log pseudolikelihood = {res:-321.28705}  
Iteration 2:{space 3}log pseudolikelihood = {res:-321.14382}  
Iteration 3:{space 3}log pseudolikelihood = {res:-321.14346}  
Iteration 4:{space 3}log pseudolikelihood = {res:-321.14346}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     46.16
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-321.14346{txt}{col 49}Pseudo R2{col 67}= {res}    0.0183

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  econ_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}economic_o {c |}{col 14}{res}{space 2} .3826018{col 26}{space 2} .2170802{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0428675{col 67}{space 3} .8080712
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2} .3430611{col 26}{space 2} .2876254{col 37}{space 1}    1.19{col 46}{space 3}0.233{col 54}{space 4}-.2206744{col 67}{space 3} .9067965
{txt}{space 4}rightist {c |}{col 14}{res}{space 2}-.2492169{col 26}{space 2} .2630896{col 37}{space 1}   -0.95{col 46}{space 3}0.344{col 54}{space 4} -.764863{col 67}{space 3} .2664292
{txt}{space 4}audience {c |}{col 14}{res}{space 2} .6053545{col 26}{space 2} .2721161{col 37}{space 1}    2.22{col 46}{space 3}0.026{col 54}{space 4} .0720167{col 67}{space 3} 1.138692
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.7173686{col 26}{space 2} .2955127{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4}-1.296563{col 67}{space 3}-.1381743
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .1449381{col 26}{space 2} .2531726{col 37}{space 1}    0.57{col 46}{space 3}0.567{col 54}{space 4}-.3512711{col 67}{space 3} .6411473
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.256769{col 26}{space 2} .4087667{col 37}{space 1}    3.07{col 46}{space 3}0.002{col 54}{space 4} .4556013{col 67}{space 3} 2.057937
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2} .9237605{col 26}{space 2} .2014584{col 54}{space 4} .5289093{col 67}{space 3} 1.318612
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} 2.518744{col 26}{space 2} .5074222{col 54}{space 4}  1.69708{col 67}{space 3} 3.738228
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-327.1446{col 38}-321.1435{col 49}     8{col 58} 658.2869{col 69} 681.5882
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg terr_frame territorial_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-618.58294}  
Iteration 1:{space 3}log pseudolikelihood = {res:-618.58227}  
Iteration 2:{space 3}log pseudolikelihood = {res:-618.58227}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-401.38386}  
Iteration 1:{space 3}log pseudolikelihood = {res:-401.37947}  
Iteration 2:{space 3}log pseudolikelihood = {res:-401.37947}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -390.2563}  
Iteration 1:{space 3}log pseudolikelihood = {res:-387.99434}  
Iteration 2:{space 3}log pseudolikelihood = {res:-387.78978}  
Iteration 3:{space 3}log pseudolikelihood = {res:-387.78925}  
Iteration 4:{space 3}log pseudolikelihood = {res:-387.78925}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     28.89
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-387.78925{txt}{col 49}Pseudo R2{col 67}= {res}    0.0339

{txt}{ralign 79:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}   terr_frame{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
territorial_o {c |}{col 15}{res}{space 2}-.4033013{col 27}{space 2} .3622498{col 38}{space 1}   -1.11{col 47}{space 3}0.266{col 55}{space 4}-1.113298{col 68}{space 3} .3066952
{txt}{space 5}cold_war {c |}{col 15}{res}{space 2}  1.09193{col 27}{space 2} .3550282{col 38}{space 1}    3.08{col 47}{space 3}0.002{col 55}{space 4} .3960871{col 68}{space 3} 1.787772
{txt}{space 5}rightist {c |}{col 15}{res}{space 2}-.2206786{col 27}{space 2} .3569748{col 38}{space 1}   -0.62{col 47}{space 3}0.536{col 55}{space 4}-.9203363{col 68}{space 3} .4789792
{txt}{space 5}audience {c |}{col 15}{res}{space 2}-.0630581{col 27}{space 2} .1890259{col 38}{space 1}   -0.33{col 47}{space 3}0.739{col 55}{space 4}-.4335421{col 68}{space 3} .3074259
{txt}{space 8}onset {c |}{col 15}{res}{space 2} -.248448{col 27}{space 2} .1791218{col 38}{space 1}   -1.39{col 47}{space 3}0.165{col 55}{space 4}-.5995203{col 68}{space 3} .1026243
{txt}{space 2}num_motives {c |}{col 15}{res}{space 2} .3186444{col 27}{space 2} .2646539{col 38}{space 1}    1.20{col 47}{space 3}0.229{col 55}{space 4}-.2000678{col 68}{space 3} .8373566
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.010538{col 27}{space 2}  .358061{col 38}{space 1}    2.82{col 47}{space 3}0.005{col 55}{space 4} .3087518{col 68}{space 3} 1.712325
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}/lnalpha {c |}{col 15}{res}{space 2}-.2417452{col 27}{space 2} .2136324{col 55}{space 4}-.6604569{col 68}{space 3} .1769666
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        alpha {c |}{col 15}{res}{space 2} .7852563{col 27}{space 2} .1677562{col 55}{space 4} .5166152{col 68}{space 3} 1.193591
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-401.3795{col 38}-387.7892{col 49}     8{col 58} 791.5785{col 69} 814.8797
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. /*Table A4. Poisson Assumption Metrics*/
. sum strat_frame human_frame terror_frame econ_frame terr_frame pro_frame multi_frame excep_frame immin_frame, detail

                         {txt}strat_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        136
{txt}25%    {res}      1.5              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}        4                      {txt}Mean          {res} 10.11765
                        {txt}Largest       Std. Dev.     {res} 14.53377
{txt}75%    {res}       13             55
{txt}90%    {res}       27             59       {txt}Variance      {res} 211.2305
{txt}95%    {res}       49             69       {txt}Skewness      {res} 2.499333
{txt}99%    {res}       69             80       {txt}Kurtosis      {res} 9.703984

                         {txt}human_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        136
{txt}25%    {res}        2              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}        6                      {txt}Mean          {res} 8.720588
                        {txt}Largest       Std. Dev.     {res} 9.484249
{txt}75%    {res}       12             33
{txt}90%    {res}       21             42       {txt}Variance      {res} 89.95098
{txt}95%    {res}       30             42       {txt}Skewness      {res} 1.706803
{txt}99%    {res}       42             47       {txt}Kurtosis      {res} 6.052502

                        {txt}terror_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        136
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}        0                      {txt}Mean          {res} 1.573529
                        {txt}Largest       Std. Dev.     {res} 4.398034
{txt}75%    {res}        0             19
{txt}90%    {res}        6             19       {txt}Variance      {res}  19.3427
{txt}95%    {res}       10             23       {txt}Skewness      {res} 3.486539
{txt}99%    {res}       23             24       {txt}Kurtosis      {res} 15.05051

                         {txt}econ_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        136
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}        1                      {txt}Mean          {res} 4.463235
                        {txt}Largest       Std. Dev.     {res} 8.200188
{txt}75%    {res}        5             33
{txt}90%    {res}       13             45       {txt}Variance      {res} 67.24308
{txt}95%    {res}       19             45       {txt}Skewness      {res} 3.326582
{txt}99%    {res}       45             46       {txt}Kurtosis      {res} 15.53735

                         {txt}terr_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        136
{txt}25%    {res}        2              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}        4                      {txt}Mean          {res} 6.544118
                        {txt}Largest       Std. Dev.     {res} 7.999877
{txt}75%    {res}      7.5             29
{txt}90%    {res}       17             31       {txt}Variance      {res} 63.99804
{txt}95%    {res}       22             34       {txt}Skewness      {res} 2.861728
{txt}99%    {res}       34             56       {txt}Kurtosis      {res} 14.29524

                          {txt}pro_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        136
{txt}25%    {res}        1              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}        4                      {txt}Mean          {res} 7.036765
                        {txt}Largest       Std. Dev.     {res} 9.889319
{txt}75%    {res}        9             32
{txt}90%    {res}       17             40       {txt}Variance      {res} 97.79864
{txt}95%    {res}       24             44       {txt}Skewness      {res} 3.953331
{txt}99%    {res}       44             80       {txt}Kurtosis      {res} 25.30055

                         {txt}multi_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        2              0       {txt}Obs         {res}        136
{txt}25%    {res}        4              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}        9                      {txt}Mean          {res} 13.33824
                        {txt}Largest       Std. Dev.     {res} 14.41615
{txt}75%    {res}       17             53
{txt}90%    {res}       28             57       {txt}Variance      {res} 207.8255
{txt}95%    {res}       47             70       {txt}Skewness      {res} 2.597765
{txt}99%    {res}       70             97       {txt}Kurtosis      {res} 12.39359

                         {txt}excep_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        136
{txt}25%    {res}      1.5              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}      5.5                      {txt}Mean          {res} 11.39706
                        {txt}Largest       Std. Dev.     {res} 15.25015
{txt}75%    {res}     14.5             55
{txt}90%    {res}       27             67       {txt}Variance      {res} 232.5671
{txt}95%    {res}       51             67       {txt}Skewness      {res} 2.258904
{txt}99%    {res}       67             83       {txt}Kurtosis      {res}   8.5248

                         {txt}immin_frame
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        2              0       {txt}Obs         {res}        136
{txt}25%    {res}        3              0       {txt}Sum of Wgt. {res}        136

{txt}50%    {res}        5                      {txt}Mean          {res} 8.948529
                        {txt}Largest       Std. Dev.     {res} 10.64882
{txt}75%    {res}       10             32
{txt}90%    {res}       19             41       {txt}Variance      {res} 113.3973
{txt}95%    {res}       27             62       {txt}Skewness      {res} 3.654442
{txt}99%    {res}       62             81       {txt}Kurtosis      {res} 21.45743
{txt}
{com}. 
. /*Table A5. Predictors of Executive Frames Justifying Military Intervention - Binary IVs*/
. nbreg strat_frame strategic $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1005.8733}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1004.0603}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1004.0602}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-457.25207}  
Iteration 1:{space 3}log pseudolikelihood = {res:-451.61507}  
Iteration 2:{space 3}log pseudolikelihood = {res:-451.60188}  
Iteration 3:{space 3}log pseudolikelihood = {res:-451.60188}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-434.23588}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.46828}  
Iteration 2:{space 3}log pseudolikelihood = {res:-428.85855}  
Iteration 3:{space 3}log pseudolikelihood = {res:-428.85492}  
Iteration 4:{space 3}log pseudolikelihood = {res:-428.85492}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    353.92
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-428.85492{txt}{col 49}Pseudo R2{col 67}= {res}    0.0504

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} strat_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}strategic {c |}{col 14}{res}{space 2} 1.997421{col 26}{space 2} .3760733{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} 1.260331{col 67}{space 3} 2.734511
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2} .1448432{col 26}{space 2} .3002478{col 37}{space 1}    0.48{col 46}{space 3}0.630{col 54}{space 4}-.4436317{col 67}{space 3} .7333181
{txt}{space 4}rightist {c |}{col 14}{res}{space 2} .1516513{col 26}{space 2} .2799604{col 37}{space 1}    0.54{col 46}{space 3}0.588{col 54}{space 4} -.397061{col 67}{space 3} .7003635
{txt}{space 4}audience {c |}{col 14}{res}{space 2}-.1948018{col 26}{space 2} .1967951{col 37}{space 1}   -0.99{col 46}{space 3}0.322{col 54}{space 4}-.5805132{col 67}{space 3} .1909095
{txt}{space 7}onset {c |}{col 14}{res}{space 2} .0064585{col 26}{space 2} .2449866{col 37}{space 1}    0.03{col 46}{space 3}0.979{col 54}{space 4}-.4737064{col 67}{space 3} .4866233
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} -.234712{col 26}{space 2} .2169161{col 37}{space 1}   -1.08{col 46}{space 3}0.279{col 54}{space 4}-.6598597{col 67}{space 3} .1904357
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8316679{col 26}{space 2} .2448244{col 37}{space 1}    3.40{col 46}{space 3}0.001{col 54}{space 4} .3518209{col 67}{space 3} 1.311515
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2} .0795243{col 26}{space 2} .1654062{col 54}{space 4}-.2446659{col 67}{space 3} .4037146
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} 1.082772{col 26}{space 2} .1790972{col 54}{space 4} .7829661{col 67}{space 3} 1.497376
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-451.6019{col 38}-428.8549{col 49}     8{col 58} 873.7098{col 69} 897.0111
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg human_frame humanitarian $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-710.22934}  
Iteration 1:{space 3}log pseudolikelihood = {res:-710.22928}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-438.04897}  
Iteration 1:{space 3}log pseudolikelihood = {res:-437.50518}  
Iteration 2:{space 3}log pseudolikelihood = {res:-437.50505}  
Iteration 3:{space 3}log pseudolikelihood = {res:-437.50505}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-427.01596}  
Iteration 1:{space 3}log pseudolikelihood = {res:-425.22827}  
Iteration 2:{space 3}log pseudolikelihood = {res:-425.11855}  
Iteration 3:{space 3}log pseudolikelihood = {res:-425.11834}  
Iteration 4:{space 3}log pseudolikelihood = {res:-425.11834}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     40.05
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-425.11834{txt}{col 49}Pseudo R2{col 67}= {res}    0.0283

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} human_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanitarian {c |}{col 14}{res}{space 2} .7003892{col 26}{space 2} .1952415{col 37}{space 1}    3.59{col 46}{space 3}0.000{col 54}{space 4} .3177229{col 67}{space 3} 1.083055
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2}-.4385985{col 26}{space 2} .1466927{col 37}{space 1}   -2.99{col 46}{space 3}0.003{col 54}{space 4}-.7261109{col 67}{space 3}-.1510861
{txt}{space 4}rightist {c |}{col 14}{res}{space 2} .1297725{col 26}{space 2} .1504486{col 37}{space 1}    0.86{col 46}{space 3}0.388{col 54}{space 4}-.1651014{col 67}{space 3} .4246464
{txt}{space 4}audience {c |}{col 14}{res}{space 2}-.1717588{col 26}{space 2} .2278148{col 37}{space 1}   -0.75{col 46}{space 3}0.451{col 54}{space 4}-.6182677{col 67}{space 3} .2747501
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.4193549{col 26}{space 2} .2479193{col 37}{space 1}   -1.69{col 46}{space 3}0.091{col 54}{space 4}-.9052677{col 67}{space 3} .0665579
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .0270898{col 26}{space 2} .1313947{col 37}{space 1}    0.21{col 46}{space 3}0.837{col 54}{space 4}-.2304391{col 67}{space 3} .2846187
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.31976{col 26}{space 2} .3467179{col 37}{space 1}    6.69{col 46}{space 3}0.000{col 54}{space 4} 1.640205{col 67}{space 3} 2.999314
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-.0755128{col 26}{space 2} .1838623{col 54}{space 4}-.4358763{col 67}{space 3} .2848507
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} .9272678{col 26}{space 2} .1704896{col 54}{space 4} .6466977{col 67}{space 3} 1.329563
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-437.5051{col 38}-425.1183{col 49}     8{col 58} 866.2367{col 69} 889.5379
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg terror_frame counterterror $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-428.95723}  
Iteration 1:{space 3}log pseudolikelihood = {res:-305.32986}  
Iteration 2:{space 3}log pseudolikelihood = {res:-281.45551}  
Iteration 3:{space 3}log pseudolikelihood = {res:-281.38324}  
Iteration 4:{space 3}log pseudolikelihood = {res:-281.38322}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-233.83667}  
Iteration 1:{space 3}log pseudolikelihood = {res: -167.9739}  
Iteration 2:{space 3}log pseudolikelihood = {res:-164.93381}  
Iteration 3:{space 3}log pseudolikelihood = {res:-164.83702}  
Iteration 4:{space 3}log pseudolikelihood = {res:-164.83698}  
Iteration 5:{space 3}log pseudolikelihood = {res:-164.83698}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-156.63841}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-148.37229}  
Iteration 2:{space 3}log pseudolikelihood = {res:-144.08703}  
Iteration 3:{space 3}log pseudolikelihood = {res:-143.12544}  
Iteration 4:{space 3}log pseudolikelihood = {res: -143.1191}  
Iteration 5:{space 3}log pseudolikelihood = {res: -143.1191}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    300.86
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -143.1191{txt}{col 49}Pseudo R2{col 67}= {res}    0.1318

{txt}{ralign 79:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} terror_frame{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
counterterror {c |}{col 15}{res}{space 2} 2.365281{col 27}{space 2}  .685336{col 38}{space 1}    3.45{col 47}{space 3}0.001{col 55}{space 4} 1.022047{col 68}{space 3} 3.708515
{txt}{space 5}cold_war {c |}{col 15}{res}{space 2}-.9864667{col 27}{space 2} .4700775{col 38}{space 1}   -2.10{col 47}{space 3}0.036{col 55}{space 4}-1.907802{col 68}{space 3}-.0651317
{txt}{space 5}rightist {c |}{col 15}{res}{space 2} 1.892649{col 27}{space 2}  .548678{col 38}{space 1}    3.45{col 47}{space 3}0.001{col 55}{space 4} .8172595{col 68}{space 3} 2.968038
{txt}{space 5}audience {c |}{col 15}{res}{space 2}-1.516078{col 27}{space 2} .2730966{col 38}{space 1}   -5.55{col 47}{space 3}0.000{col 55}{space 4}-2.051338{col 68}{space 3}-.9808187
{txt}{space 8}onset {c |}{col 15}{res}{space 2}-.5807622{col 27}{space 2} .2712325{col 38}{space 1}   -2.14{col 47}{space 3}0.032{col 55}{space 4}-1.112368{col 68}{space 3}-.0491563
{txt}{space 2}num_motives {c |}{col 15}{res}{space 2} 1.230199{col 27}{space 2} .3412598{col 38}{space 1}    3.60{col 47}{space 3}0.000{col 55}{space 4} .5613422{col 68}{space 3} 1.899056
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} -2.34365{col 27}{space 2} .8555427{col 38}{space 1}   -2.74{col 47}{space 3}0.006{col 55}{space 4}-4.020483{col 68}{space 3}-.6668173
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}/lnalpha {c |}{col 15}{res}{space 2} 1.235337{col 27}{space 2} .1802708{col 55}{space 4}  .882013{col 68}{space 3} 1.588661
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        alpha {c |}{col 15}{res}{space 2} 3.439538{col 27}{space 2} .6200481{col 55}{space 4} 2.415758{col 68}{space 3} 4.897189
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27} -164.837{col 38}-143.1191{col 49}     8{col 58} 302.2382{col 69} 325.5394
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg econ_frame economic $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-702.12945}  
Iteration 1:{space 3}log pseudolikelihood = {res:-702.12812}  
Iteration 2:{space 3}log pseudolikelihood = {res:-702.12812}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-353.64912}  
Iteration 1:{space 3}log pseudolikelihood = {res:-327.21439}  
Iteration 2:{space 3}log pseudolikelihood = {res:-327.14462}  
Iteration 3:{space 3}log pseudolikelihood = {res:-327.14461}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-322.60117}  
Iteration 1:{space 3}log pseudolikelihood = {res:-321.28705}  
Iteration 2:{space 3}log pseudolikelihood = {res:-321.14382}  
Iteration 3:{space 3}log pseudolikelihood = {res:-321.14346}  
Iteration 4:{space 3}log pseudolikelihood = {res:-321.14346}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     46.16
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-321.14346{txt}{col 49}Pseudo R2{col 67}= {res}    0.0183

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  econ_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}economic {c |}{col 14}{res}{space 2} .3826018{col 26}{space 2} .2170802{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0428675{col 67}{space 3} .8080712
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2} .3430611{col 26}{space 2} .2876254{col 37}{space 1}    1.19{col 46}{space 3}0.233{col 54}{space 4}-.2206744{col 67}{space 3} .9067965
{txt}{space 4}rightist {c |}{col 14}{res}{space 2}-.2492169{col 26}{space 2} .2630896{col 37}{space 1}   -0.95{col 46}{space 3}0.344{col 54}{space 4} -.764863{col 67}{space 3} .2664292
{txt}{space 4}audience {c |}{col 14}{res}{space 2} .6053545{col 26}{space 2} .2721161{col 37}{space 1}    2.22{col 46}{space 3}0.026{col 54}{space 4} .0720167{col 67}{space 3} 1.138692
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.7173686{col 26}{space 2} .2955127{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4}-1.296563{col 67}{space 3}-.1381743
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .1449381{col 26}{space 2} .2531726{col 37}{space 1}    0.57{col 46}{space 3}0.567{col 54}{space 4}-.3512711{col 67}{space 3} .6411473
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.256769{col 26}{space 2} .4087667{col 37}{space 1}    3.07{col 46}{space 3}0.002{col 54}{space 4} .4556013{col 67}{space 3} 2.057937
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2} .9237605{col 26}{space 2} .2014584{col 54}{space 4} .5289093{col 67}{space 3} 1.318612
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} 2.518744{col 26}{space 2} .5074222{col 54}{space 4}  1.69708{col 67}{space 3} 3.738228
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-327.1446{col 38}-321.1435{col 49}     8{col 58} 658.2869{col 69} 681.5882
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg terr_frame territorial $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-619.64777}  
Iteration 1:{space 3}log pseudolikelihood = {res:-619.64711}  
Iteration 2:{space 3}log pseudolikelihood = {res:-619.64711}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-401.38386}  
Iteration 1:{space 3}log pseudolikelihood = {res:-401.37947}  
Iteration 2:{space 3}log pseudolikelihood = {res:-401.37947}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-390.48857}  
Iteration 1:{space 3}log pseudolikelihood = {res:-388.35265}  
Iteration 2:{space 3}log pseudolikelihood = {res: -388.1774}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -388.177}  
Iteration 4:{space 3}log pseudolikelihood = {res:  -388.177}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     14.85
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0215
{txt}Log pseudolikelihood = {res}  -388.177{txt}{col 49}Pseudo R2{col 67}= {res}    0.0329

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  terr_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}territorial {c |}{col 14}{res}{space 2}-.6120998{col 26}{space 2} .7691863{col 37}{space 1}   -0.80{col 46}{space 3}0.426{col 54}{space 4}-2.119677{col 67}{space 3} .8954776
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2} 1.062438{col 26}{space 2} .3485926{col 37}{space 1}    3.05{col 46}{space 3}0.002{col 54}{space 4} .3792088{col 67}{space 3} 1.745667
{txt}{space 4}rightist {c |}{col 14}{res}{space 2}-.1741549{col 26}{space 2}  .334239{col 37}{space 1}   -0.52{col 46}{space 3}0.602{col 54}{space 4}-.8292513{col 67}{space 3} .4809415
{txt}{space 4}audience {c |}{col 14}{res}{space 2}-.0637649{col 26}{space 2} .1937783{col 37}{space 1}   -0.33{col 46}{space 3}0.742{col 54}{space 4}-.4435634{col 67}{space 3} .3160336
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.2237354{col 26}{space 2} .1723135{col 37}{space 1}   -1.30{col 46}{space 3}0.194{col 54}{space 4}-.5614636{col 67}{space 3} .1139927
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .2913112{col 26}{space 2} .2850758{col 37}{space 1}    1.02{col 46}{space 3}0.307{col 54}{space 4}-.2674271{col 67}{space 3} .8500495
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.025309{col 26}{space 2} .3942268{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4} .2526389{col 67}{space 3} 1.797979
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-.2359295{col 26}{space 2} .2116315{col 54}{space 4}-.6507195{col 67}{space 3} .1788606
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} .7898364{col 26}{space 2} .1671542{col 54}{space 4} .5216703{col 67}{space 3} 1.195854
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-401.3795{col 38} -388.177{col 49}     8{col 58}  792.354{col 69} 815.6552
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. /*Table A6. Robustness Check Excluding Congressional Remarks*/
. drop if audience == 1
{txt}(43 observations deleted)

{com}. 
. nbreg strat_frame strategic_o cold_war rightist onset num_motives, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-603.43253}  
Iteration 1:{space 3}log pseudolikelihood = {res:-602.21742}  
Iteration 2:{space 3}log pseudolikelihood = {res:-602.21601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-602.21601}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-316.29007}  
Iteration 1:{space 3}log pseudolikelihood = {res:-313.16614}  
Iteration 2:{space 3}log pseudolikelihood = {res:-313.15597}  
Iteration 3:{space 3}log pseudolikelihood = {res:-313.15597}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-297.87827}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.04889}  
Iteration 2:{space 3}log pseudolikelihood = {res: -291.2244}  
Iteration 3:{space 3}log pseudolikelihood = {res:-291.12188}  
Iteration 4:{space 3}log pseudolikelihood = {res:-291.12181}  
Iteration 5:{space 3}log pseudolikelihood = {res:-291.12181}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}        93
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}    116.76
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-291.12181{txt}{col 49}Pseudo R2{col 67}= {res}    0.0704

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} strat_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}strategic_o {c |}{col 14}{res}{space 2} 1.022079{col 26}{space 2} .1756077{col 37}{space 1}    5.82{col 46}{space 3}0.000{col 54}{space 4} .6778946{col 67}{space 3} 1.366264
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2}   .10714{col 26}{space 2}  .266579{col 37}{space 1}    0.40{col 46}{space 3}0.688{col 54}{space 4}-.4153453{col 67}{space 3} .6296253
{txt}{space 4}rightist {c |}{col 14}{res}{space 2}-.0644328{col 26}{space 2} .3105939{col 37}{space 1}   -0.21{col 46}{space 3}0.836{col 54}{space 4}-.6731856{col 67}{space 3} .5443201
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.2544922{col 26}{space 2} .2074947{col 37}{space 1}   -1.23{col 46}{space 3}0.220{col 54}{space 4}-.6611744{col 67}{space 3}   .15219
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .1822857{col 26}{space 2}   .15497{col 37}{space 1}    1.18{col 46}{space 3}0.239{col 54}{space 4}-.1214498{col 67}{space 3} .4860213
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5065901{col 26}{space 2} .2924989{col 37}{space 1}    1.73{col 46}{space 3}0.083{col 54}{space 4}-.0666973{col 67}{space 3} 1.079878
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-.1122743{col 26}{space 2} .1977451{col 54}{space 4}-.4998475{col 67}{space 3}  .275299
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} .8937991{col 26}{space 2} .1767444{col 54}{space 4} .6066231{col 67}{space 3} 1.316924
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}        93{col 27} -313.156{col 38}-291.1218{col 49}     7{col 58} 596.2436{col 69} 613.9718
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg human_frame humanitarian_o cold_war rightist onset num_motives, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-478.60138}  
Iteration 1:{space 3}log pseudolikelihood = {res:-478.59946}  
Iteration 2:{space 3}log pseudolikelihood = {res:-478.59946}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-305.74059}  
Iteration 1:{space 3}log pseudolikelihood = {res:-304.67969}  
Iteration 2:{space 3}log pseudolikelihood = {res:-304.67898}  
Iteration 3:{space 3}log pseudolikelihood = {res:-304.67898}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-296.73735}  
Iteration 1:{space 3}log pseudolikelihood = {res:-295.20387}  
Iteration 2:{space 3}log pseudolikelihood = {res:-295.05673}  
Iteration 3:{space 3}log pseudolikelihood = {res:-295.05622}  
Iteration 4:{space 3}log pseudolikelihood = {res:-295.05622}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}        93
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     47.11
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-295.05622{txt}{col 49}Pseudo R2{col 67}= {res}    0.0316

{txt}{ralign 80:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}   human_frame{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanitarian_o {c |}{col 16}{res}{space 2} .3046068{col 28}{space 2} .1372671{col 39}{space 1}    2.22{col 48}{space 3}0.026{col 56}{space 4} .0355683{col 69}{space 3} .5736454
{txt}{space 6}cold_war {c |}{col 16}{res}{space 2}-.6669381{col 28}{space 2} .1504109{col 39}{space 1}   -4.43{col 48}{space 3}0.000{col 56}{space 4} -.961738{col 69}{space 3}-.3721382
{txt}{space 6}rightist {c |}{col 16}{res}{space 2}-.0418484{col 28}{space 2} .1936251{col 39}{space 1}   -0.22{col 48}{space 3}0.829{col 56}{space 4}-.4213466{col 69}{space 3} .3376498
{txt}{space 9}onset {c |}{col 16}{res}{space 2}-.3094248{col 28}{space 2} .2235682{col 39}{space 1}   -1.38{col 48}{space 3}0.166{col 56}{space 4}-.7476105{col 69}{space 3} .1287609
{txt}{space 3}num_motives {c |}{col 16}{res}{space 2} .2212423{col 28}{space 2} .1596589{col 39}{space 1}    1.39{col 48}{space 3}0.166{col 56}{space 4}-.0916835{col 69}{space 3}  .534168
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.184011{col 28}{space 2}  .350196{col 39}{space 1}    6.24{col 48}{space 3}0.000{col 56}{space 4} 1.497639{col 69}{space 3} 2.870383
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2}-.0232448{col 28}{space 2} .2375117{col 56}{space 4}-.4887593{col 69}{space 3} .4422696
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} .9770233{col 28}{space 2} .2320545{col 56}{space 4}  .613387{col 69}{space 3} 1.556235
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}        93{col 27} -304.679{col 38}-295.0562{col 49}     7{col 58} 604.1124{col 69} 621.8406
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg terror_frame counterterror_o cold_war rightist onset num_motives, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-410.95412}  
Iteration 1:{space 3}log pseudolikelihood = {res:-287.29919}  
Iteration 2:{space 3}log pseudolikelihood = {res:-248.16647}  
Iteration 3:{space 3}log pseudolikelihood = {res:-247.97584}  
Iteration 4:{space 3}log pseudolikelihood = {res:-247.97571}  
Iteration 5:{space 3}log pseudolikelihood = {res:-247.97571}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-179.98855}  
Iteration 1:{space 3}log pseudolikelihood = {res:-131.73671}  
Iteration 2:{space 3}log pseudolikelihood = {res:-131.41383}  
Iteration 3:{space 3}log pseudolikelihood = {res:-131.41302}  
Iteration 4:{space 3}log pseudolikelihood = {res:-131.41302}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-125.56222}  
Iteration 1:{space 3}log pseudolikelihood = {res:-119.02426}  
Iteration 2:{space 3}log pseudolikelihood = {res: -117.1063}  
Iteration 3:{space 3}log pseudolikelihood = {res:-117.02658}  
Iteration 4:{space 3}log pseudolikelihood = {res: -117.0265}  
Iteration 5:{space 3}log pseudolikelihood = {res: -117.0265}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}        93
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     30.70
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -117.0265{txt}{col 49}Pseudo R2{col 67}= {res}    0.1095

{txt}{ralign 81:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   terror_frame{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
counterterror_o {c |}{col 17}{res}{space 2} 1.407338{col 29}{space 2}  .369503{col 40}{space 1}    3.81{col 49}{space 3}0.000{col 57}{space 4} .6831253{col 70}{space 3}  2.13155
{txt}{space 7}cold_war {c |}{col 17}{res}{space 2}-.9883026{col 29}{space 2} .4615836{col 40}{space 1}   -2.14{col 49}{space 3}0.032{col 57}{space 4} -1.89299{col 70}{space 3}-.0836155
{txt}{space 7}rightist {c |}{col 17}{res}{space 2} 1.842931{col 29}{space 2} .4972134{col 40}{space 1}    3.71{col 49}{space 3}0.000{col 57}{space 4} .8684107{col 70}{space 3} 2.817451
{txt}{space 10}onset {c |}{col 17}{res}{space 2}-.9175721{col 29}{space 2} .2621342{col 40}{space 1}   -3.50{col 49}{space 3}0.000{col 57}{space 4}-1.431346{col 70}{space 3}-.4037984
{txt}{space 4}num_motives {c |}{col 17}{res}{space 2} 1.059097{col 29}{space 2} .3713519{col 40}{space 1}    2.85{col 49}{space 3}0.004{col 57}{space 4} .3312604{col 70}{space 3} 1.786933
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.834192{col 29}{space 2} .9189578{col 40}{space 1}   -2.00{col 49}{space 3}0.046{col 57}{space 4}-3.635316{col 70}{space 3}-.0330676
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2} 1.218291{col 29}{space 2} .1854924{col 57}{space 4}  .854733{col 70}{space 3}  1.58185
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 3.381405{col 29}{space 2} .6272248{col 57}{space 4} 2.350747{col 70}{space 3} 4.863944
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}        93{col 27} -131.413{col 38}-117.0265{col 49}     7{col 58}  248.053{col 69} 265.7812
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg econ_frame economic_o cold_war rightist onset num_motives, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-334.36874}  
Iteration 1:{space 3}log pseudolikelihood = {res:-334.36776}  
Iteration 2:{space 3}log pseudolikelihood = {res:-334.36776}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-225.25617}  
Iteration 1:{space 3}log pseudolikelihood = {res:-219.72044}  
Iteration 2:{space 3}log pseudolikelihood = {res:-219.71575}  
Iteration 3:{space 3}log pseudolikelihood = {res:-219.71575}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -215.361}  
Iteration 1:{space 3}log pseudolikelihood = {res:-214.46313}  
Iteration 2:{space 3}log pseudolikelihood = {res: -214.4037}  
Iteration 3:{space 3}log pseudolikelihood = {res: -214.4036}  
Iteration 4:{space 3}log pseudolikelihood = {res: -214.4036}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}        93
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     20.38
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0011
{txt}Log pseudolikelihood = {res} -214.4036{txt}{col 49}Pseudo R2{col 67}= {res}    0.0242

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  econ_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}economic_o {c |}{col 14}{res}{space 2} .2468877{col 26}{space 2} .2680061{col 37}{space 1}    0.92{col 46}{space 3}0.357{col 54}{space 4}-.2783946{col 67}{space 3} .7721699
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2}-.0182708{col 26}{space 2} .2572077{col 37}{space 1}   -0.07{col 46}{space 3}0.943{col 54}{space 4}-.5223886{col 67}{space 3} .4858471
{txt}{space 4}rightist {c |}{col 14}{res}{space 2}-.3991853{col 26}{space 2} .2454005{col 37}{space 1}   -1.63{col 46}{space 3}0.104{col 54}{space 4}-.8801614{col 67}{space 3} .0817908
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.5837357{col 26}{space 2} .3219376{col 37}{space 1}   -1.81{col 46}{space 3}0.070{col 54}{space 4}-1.214722{col 67}{space 3} .0472504
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .3845747{col 26}{space 2} .2338009{col 37}{space 1}    1.64{col 46}{space 3}0.100{col 54}{space 4}-.0736666{col 67}{space 3}  .842816
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.064824{col 26}{space 2}  .310737{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .4557904{col 67}{space 3} 1.673857
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2} .4703685{col 26}{space 2}  .245738{col 54}{space 4}-.0112692{col 67}{space 3} .9520061
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} 1.600584{col 26}{space 2} .3933243{col 54}{space 4}  .988794{col 67}{space 3} 2.590902
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}        93{col 27}-219.7158{col 38}-214.4036{col 49}     7{col 58} 442.8072{col 69} 460.5354
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg terr_frame territorial_o cold_war rightist onset num_motives, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -393.5816}  
Iteration 1:{space 3}log pseudolikelihood = {res:-393.58127}  
Iteration 2:{space 3}log pseudolikelihood = {res:-393.58127}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-274.53217}  
Iteration 1:{space 3}log pseudolikelihood = {res:-274.30363}  
Iteration 2:{space 3}log pseudolikelihood = {res:-274.30362}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-266.63662}  
Iteration 1:{space 3}log pseudolikelihood = {res:-265.34382}  
Iteration 2:{space 3}log pseudolikelihood = {res:-265.27471}  
Iteration 3:{space 3}log pseudolikelihood = {res:-265.27462}  
Iteration 4:{space 3}log pseudolikelihood = {res:-265.27462}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}        93
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     56.42
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-265.27462{txt}{col 49}Pseudo R2{col 67}= {res}    0.0329

{txt}{ralign 79:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}   terr_frame{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
territorial_o {c |}{col 15}{res}{space 2}-.2187175{col 27}{space 2} .3723003{col 38}{space 1}   -0.59{col 47}{space 3}0.557{col 55}{space 4}-.9484126{col 68}{space 3} .5109776
{txt}{space 5}cold_war {c |}{col 15}{res}{space 2} .9236879{col 27}{space 2} .3332922{col 38}{space 1}    2.77{col 47}{space 3}0.006{col 55}{space 4} .2704473{col 68}{space 3} 1.576928
{txt}{space 5}rightist {c |}{col 15}{res}{space 2}-.0694203{col 27}{space 2} .3338931{col 38}{space 1}   -0.21{col 47}{space 3}0.835{col 55}{space 4}-.7238388{col 68}{space 3} .5849983
{txt}{space 8}onset {c |}{col 15}{res}{space 2}-.0498954{col 27}{space 2} .1857786{col 38}{space 1}   -0.27{col 47}{space 3}0.788{col 55}{space 4}-.4140148{col 68}{space 3}  .314224
{txt}{space 2}num_motives {c |}{col 15}{res}{space 2} .2910943{col 27}{space 2} .3198076{col 38}{space 1}    0.91{col 47}{space 3}0.363{col 55}{space 4}-.3357171{col 68}{space 3} .9179058
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .9066436{col 27}{space 2} .4834135{col 38}{space 1}    1.88{col 47}{space 3}0.061{col 55}{space 4}-.0408294{col 68}{space 3} 1.854117
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}/lnalpha {c |}{col 15}{res}{space 2}-.3530768{col 27}{space 2} .2275261{col 55}{space 4}-.7990197{col 68}{space 3} .0928661
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        alpha {c |}{col 15}{res}{space 2} .7025232{col 27}{space 2} .1598423{col 55}{space 4} .4497696{col 68}{space 3} 1.097315
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}        93{col 27}-274.3036{col 38}-265.2746{col 49}     7{col 58} 544.5492{col 69} 562.2774
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. clear all
{res}{txt}
{com}. 
{txt}end of do-file

{com}. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Kerry\Documents\Alchemy\Publications\230915 - framil\Publication\Replication files\framil_chavez_replicationlog.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res}19 Sep 2023, 17:08:13
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Kerry\Documents\Alchemy\Publications\230915 - framil\Publication\Replication files\framil_chavez_replicationlog.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res}19 Sep 2023, 17:08:23

{com}. do "C:\Users\Kerry\AppData\Local\Temp\STD2978_000000.tmp"
{txt}
{com}. global c = "cold_war rightist audience onset num_motives"
{txt}
{com}. 
. /*Table A7. Exploratory Predictors of Proliferation Frames Justifying Military Intervention*/
. nbreg pro_frame strategic_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-612.18527}  
Iteration 1:{space 3}log pseudolikelihood = {res: -612.1557}  
Iteration 2:{space 3}log pseudolikelihood = {res: -612.1557}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-410.59196}  
Iteration 1:{space 3}log pseudolikelihood = {res: -409.9672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-409.96707}  
Iteration 3:{space 3}log pseudolikelihood = {res:-409.96707}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-392.60676}  
Iteration 1:{space 3}log pseudolikelihood = {res:-388.85107}  
Iteration 2:{space 3}log pseudolikelihood = {res:-386.29037}  
Iteration 3:{space 3}log pseudolikelihood = {res: -386.2745}  
Iteration 4:{space 3}log pseudolikelihood = {res: -386.2745}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    270.44
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -386.2745{txt}{col 49}Pseudo R2{col 67}= {res}    0.0578

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   pro_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}strategic_o {c |}{col 14}{res}{space 2} .5988297{col 26}{space 2}  .126891{col 37}{space 1}    4.72{col 46}{space 3}0.000{col 54}{space 4} .3501279{col 67}{space 3} .8475314
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2}-.7476697{col 26}{space 2}  .208184{col 37}{space 1}   -3.59{col 46}{space 3}0.000{col 54}{space 4}-1.155703{col 67}{space 3}-.3396366
{txt}{space 4}rightist {c |}{col 14}{res}{space 2}-.0093883{col 26}{space 2} .2378247{col 37}{space 1}   -0.04{col 46}{space 3}0.969{col 54}{space 4}-.4755162{col 67}{space 3} .4567396
{txt}{space 4}audience {c |}{col 14}{res}{space 2} .2777218{col 26}{space 2} .2029954{col 37}{space 1}    1.37{col 46}{space 3}0.171{col 54}{space 4}-.1201418{col 67}{space 3} .6755854
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.4985684{col 26}{space 2} .1856735{col 37}{space 1}   -2.69{col 46}{space 3}0.007{col 54}{space 4}-.8624818{col 67}{space 3}-.1346549
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .1528792{col 26}{space 2} .1218548{col 37}{space 1}    1.25{col 46}{space 3}0.210{col 54}{space 4}-.0859519{col 67}{space 3} .3917102
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.381796{col 26}{space 2} .4307649{col 37}{space 1}    3.21{col 46}{space 3}0.001{col 54}{space 4} .5375123{col 67}{space 3}  2.22608
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-.2623859{col 26}{space 2} .0910137{col 54}{space 4}-.4407695{col 67}{space 3}-.0840023
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} .7692141{col 26}{space 2}  .070009{col 54}{space 4}  .643541{col 67}{space 3} .9194291
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-409.9671{col 38}-386.2745{col 49}     8{col 58}  788.549{col 69} 811.8502
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg pro_frame humanitarian_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-649.07434}  
Iteration 1:{space 3}log pseudolikelihood = {res:-649.06685}  
Iteration 2:{space 3}log pseudolikelihood = {res:-649.06685}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-410.59196}  
Iteration 1:{space 3}log pseudolikelihood = {res: -409.9672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-409.96707}  
Iteration 3:{space 3}log pseudolikelihood = {res:-409.96707}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-396.24648}  
Iteration 1:{space 3}log pseudolikelihood = {res:-392.98874}  
Iteration 2:{space 3}log pseudolikelihood = {res:-391.89501}  
Iteration 3:{space 3}log pseudolikelihood = {res:-391.89346}  
Iteration 4:{space 3}log pseudolikelihood = {res:-391.89346}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     72.57
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-391.89346{txt}{col 49}Pseudo R2{col 67}= {res}    0.0441

{txt}{ralign 80:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}     pro_frame{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanitarian_o {c |}{col 16}{res}{space 2}-.4528094{col 28}{space 2} .1611424{col 39}{space 1}   -2.81{col 48}{space 3}0.005{col 56}{space 4}-.7686427{col 69}{space 3}-.1369761
{txt}{space 6}cold_war {c |}{col 16}{res}{space 2}-.7045606{col 28}{space 2}  .274326{col 39}{space 1}   -2.57{col 48}{space 3}0.010{col 56}{space 4} -1.24223{col 69}{space 3}-.1668916
{txt}{space 6}rightist {c |}{col 16}{res}{space 2} .1379603{col 28}{space 2} .2280358{col 39}{space 1}    0.60{col 48}{space 3}0.545{col 56}{space 4}-.3089817{col 69}{space 3} .5849023
{txt}{space 6}audience {c |}{col 16}{res}{space 2} .3563648{col 28}{space 2} .1933021{col 39}{space 1}    1.84{col 48}{space 3}0.065{col 56}{space 4}-.0225003{col 69}{space 3} .7352299
{txt}{space 9}onset {c |}{col 16}{res}{space 2}-.3998774{col 28}{space 2} .1949152{col 39}{space 1}   -2.05{col 48}{space 3}0.040{col 56}{space 4}-.7819042{col 69}{space 3}-.0178507
{txt}{space 3}num_motives {c |}{col 16}{res}{space 2} .1879609{col 28}{space 2} .1804399{col 39}{space 1}    1.04{col 48}{space 3}0.298{col 56}{space 4}-.1656947{col 69}{space 3} .5416166
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.284311{col 28}{space 2} .4816354{col 39}{space 1}    4.74{col 48}{space 3}0.000{col 56}{space 4} 1.340323{col 69}{space 3} 3.228299
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2}-.1620005{col 28}{space 2} .0952786{col 56}{space 4}-.3487432{col 69}{space 3} .0247422
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} .8504408{col 28}{space 2} .0810288{col 56}{space 4} .7055743{col 69}{space 3} 1.025051
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-409.9671{col 38}-391.8935{col 49}     8{col 58} 799.7869{col 69} 823.0882
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg pro_frame counterterror_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-678.50308}  
Iteration 1:{space 3}log pseudolikelihood = {res:-678.49937}  
Iteration 2:{space 3}log pseudolikelihood = {res:-678.49937}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-410.59196}  
Iteration 1:{space 3}log pseudolikelihood = {res: -409.9672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-409.96707}  
Iteration 3:{space 3}log pseudolikelihood = {res:-409.96707}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-399.54151}  
Iteration 1:{space 3}log pseudolikelihood = {res:-397.34048}  
Iteration 2:{space 3}log pseudolikelihood = {res:-397.00407}  
Iteration 3:{space 3}log pseudolikelihood = {res:-397.00354}  
Iteration 4:{space 3}log pseudolikelihood = {res:-397.00354}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    256.92
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-397.00354{txt}{col 49}Pseudo R2{col 67}= {res}    0.0316

{txt}{ralign 81:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}      pro_frame{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
counterterror_o {c |}{col 17}{res}{space 2}-.3807461{col 29}{space 2} .1622714{col 40}{space 1}   -2.35{col 49}{space 3}0.019{col 57}{space 4}-.6987922{col 70}{space 3}-.0627001
{txt}{space 7}cold_war {c |}{col 17}{res}{space 2}-.6093156{col 29}{space 2} .2483962{col 40}{space 1}   -2.45{col 49}{space 3}0.014{col 57}{space 4}-1.096163{col 70}{space 3}-.1224681
{txt}{space 7}rightist {c |}{col 17}{res}{space 2} .1279628{col 29}{space 2} .2134925{col 40}{space 1}    0.60{col 49}{space 3}0.549{col 57}{space 4}-.2904748{col 70}{space 3} .5464004
{txt}{space 7}audience {c |}{col 17}{res}{space 2} .2529741{col 29}{space 2} .2232693{col 40}{space 1}    1.13{col 49}{space 3}0.257{col 57}{space 4}-.1846257{col 70}{space 3}  .690574
{txt}{space 10}onset {c |}{col 17}{res}{space 2}-.5328798{col 29}{space 2} .1870588{col 40}{space 1}   -2.85{col 49}{space 3}0.004{col 57}{space 4}-.8995083{col 70}{space 3}-.1662514
{txt}{space 4}num_motives {c |}{col 17}{res}{space 2} .2805052{col 29}{space 2} .1979334{col 40}{space 1}    1.42{col 49}{space 3}0.156{col 57}{space 4}-.1074372{col 70}{space 3} .6684475
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}    2.036{col 29}{space 2} .3982736{col 40}{space 1}    5.11{col 49}{space 3}0.000{col 57}{space 4} 1.255398{col 70}{space 3} 2.816602
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}-.0741864{col 29}{space 2} .1379902{col 57}{space 4}-.3446422{col 70}{space 3} .1962694
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} .9284986{col 29}{space 2} .1281237{col 57}{space 4} .7084738{col 70}{space 3} 1.216855
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-409.9671{col 38}-397.0035{col 49}     8{col 58} 810.0071{col 69} 833.3083
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg pro_frame economic_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-670.76186}  
Iteration 1:{space 3}log pseudolikelihood = {res:-670.75408}  
Iteration 2:{space 3}log pseudolikelihood = {res:-670.75408}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-410.59196}  
Iteration 1:{space 3}log pseudolikelihood = {res: -409.9672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-409.96707}  
Iteration 3:{space 3}log pseudolikelihood = {res:-409.96707}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-400.13398}  
Iteration 1:{space 3}log pseudolikelihood = {res:-397.63076}  
Iteration 2:{space 3}log pseudolikelihood = {res: -397.1114}  
Iteration 3:{space 3}log pseudolikelihood = {res:-397.11106}  
Iteration 4:{space 3}log pseudolikelihood = {res:-397.11106}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     58.34
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-397.11106{txt}{col 49}Pseudo R2{col 67}= {res}    0.0314

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   pro_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}economic_o {c |}{col 14}{res}{space 2} .4861945{col 26}{space 2} .2858972{col 37}{space 1}    1.70{col 46}{space 3}0.089{col 54}{space 4}-.0741538{col 67}{space 3} 1.046543
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2}-.4364613{col 26}{space 2}  .311432{col 37}{space 1}   -1.40{col 46}{space 3}0.161{col 54}{space 4}-1.046857{col 67}{space 3} .1739342
{txt}{space 4}rightist {c |}{col 14}{res}{space 2} .1154511{col 26}{space 2} .2422217{col 37}{space 1}    0.48{col 46}{space 3}0.634{col 54}{space 4}-.3592948{col 67}{space 3}  .590197
{txt}{space 4}audience {c |}{col 14}{res}{space 2} .3130806{col 26}{space 2} .2100896{col 37}{space 1}    1.49{col 46}{space 3}0.136{col 54}{space 4}-.0986874{col 67}{space 3} .7248487
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.4834344{col 26}{space 2} .1980763{col 37}{space 1}   -2.44{col 46}{space 3}0.015{col 54}{space 4}-.8716569{col 67}{space 3}-.0952119
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2} .0325987{col 26}{space 2} .2079993{col 37}{space 1}    0.16{col 46}{space 3}0.875{col 54}{space 4}-.3750725{col 67}{space 3} .4402699
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.083047{col 26}{space 2} .4006872{col 37}{space 1}    5.20{col 46}{space 3}0.000{col 54}{space 4} 1.297714{col 67}{space 3} 2.868379
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-.0744186{col 26}{space 2} .1262136{col 54}{space 4}-.3217928{col 67}{space 3} .1729557
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} .9282831{col 26}{space 2}  .117162{col 54}{space 4} .7248484{col 67}{space 3} 1.188813
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-409.9671{col 38}-397.1111{col 49}     8{col 58} 810.2221{col 69} 833.5234
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. nbreg pro_frame territorial_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-689.20169}  
Iteration 1:{space 3}log pseudolikelihood = {res:-689.19506}  
Iteration 2:{space 3}log pseudolikelihood = {res:-689.19506}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-410.59196}  
Iteration 1:{space 3}log pseudolikelihood = {res: -409.9672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-409.96707}  
Iteration 3:{space 3}log pseudolikelihood = {res:-409.96707}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-398.81129}  
Iteration 1:{space 3}log pseudolikelihood = {res:-396.51539}  
Iteration 2:{space 3}log pseudolikelihood = {res:-396.28248}  
Iteration 3:{space 3}log pseudolikelihood = {res: -396.2817}  
Iteration 4:{space 3}log pseudolikelihood = {res: -396.2817}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     55.91
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -396.2817{txt}{col 49}Pseudo R2{col 67}= {res}    0.0334

{txt}{ralign 79:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    pro_frame{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
territorial_o {c |}{col 15}{res}{space 2}-.5914651{col 27}{space 2} .3198754{col 38}{space 1}   -1.85{col 47}{space 3}0.064{col 55}{space 4}-1.218409{col 68}{space 3} .0354791
{txt}{space 5}cold_war {c |}{col 15}{res}{space 2}-.3091953{col 27}{space 2} .2893756{col 38}{space 1}   -1.07{col 47}{space 3}0.285{col 55}{space 4} -.876361{col 68}{space 3} .2579703
{txt}{space 5}rightist {c |}{col 15}{res}{space 2} .0053214{col 27}{space 2} .2623552{col 38}{space 1}    0.02{col 47}{space 3}0.984{col 55}{space 4}-.5088854{col 68}{space 3} .5195282
{txt}{space 5}audience {c |}{col 15}{res}{space 2} .2490522{col 27}{space 2} .2214122{col 38}{space 1}    1.12{col 47}{space 3}0.261{col 55}{space 4}-.1849077{col 68}{space 3} .6830121
{txt}{space 8}onset {c |}{col 15}{res}{space 2}-.6747097{col 27}{space 2} .2137433{col 38}{space 1}   -3.16{col 47}{space 3}0.002{col 55}{space 4}-1.093639{col 68}{space 3}-.2557806
{txt}{space 2}num_motives {c |}{col 15}{res}{space 2} .5314901{col 27}{space 2} .2323194{col 38}{space 1}    2.29{col 47}{space 3}0.022{col 55}{space 4} .0761525{col 68}{space 3} .9868277
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.671406{col 27}{space 2} .3479911{col 38}{space 1}    4.80{col 47}{space 3}0.000{col 55}{space 4} .9893562{col 68}{space 3} 2.353456
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}/lnalpha {c |}{col 15}{res}{space 2} -.082498{col 27}{space 2}  .154585{col 55}{space 4}-.3854791{col 68}{space 3} .2204831
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        alpha {c |}{col 15}{res}{space 2} .9208133{col 27}{space 2}  .142344{col 55}{space 4} .6801247{col 68}{space 3} 1.246679
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       136{col 27}-409.9671{col 38}-396.2817{col 49}     8{col 58} 808.5634{col 69} 831.8646
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. /*Table A8. Exploratory Predictors of Rhetorical Frames Justifying Military Intervention*/
. nbreg multi_frame counterterror_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1021.1937}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1021.1788}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1021.1788}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-493.30162}  
Iteration 1:{space 3}log pseudolikelihood = {res: -492.7031}  
Iteration 2:{space 3}log pseudolikelihood = {res:-492.70266}  
Iteration 3:{space 3}log pseudolikelihood = {res:-492.70266}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-488.32991}  
Iteration 1:{space 3}log pseudolikelihood = {res:-487.97804}  
Iteration 2:{space 3}log pseudolikelihood = {res:-487.97523}  
Iteration 3:{space 3}log pseudolikelihood = {res:-487.97523}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     31.15
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-487.97523{txt}{col 49}Pseudo R2{col 67}= {res}    0.0096

{txt}{ralign 81:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}    multi_frame{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
counterterror_o {c |}{col 17}{res}{space 2}-.3801929{col 29}{space 2} .0928968{col 40}{space 1}   -4.09{col 49}{space 3}0.000{col 57}{space 4}-.5622673{col 70}{space 3}-.1981185
{txt}{space 7}cold_war {c |}{col 17}{res}{space 2}  .073588{col 29}{space 2} .2710688{col 40}{space 1}    0.27{col 49}{space 3}0.786{col 57}{space 4}-.4576972{col 70}{space 3} .6048732
{txt}{space 7}rightist {c |}{col 17}{res}{space 2}-.0056012{col 29}{space 2} .2464609{col 40}{space 1}   -0.02{col 49}{space 3}0.982{col 57}{space 4}-.4886556{col 70}{space 3} .4774533
{txt}{space 7}audience {c |}{col 17}{res}{space 2} .2790881{col 29}{space 2} .1671596{col 40}{space 1}    1.67{col 49}{space 3}0.095{col 57}{space 4}-.0485387{col 70}{space 3} .6067149
{txt}{space 10}onset {c |}{col 17}{res}{space 2}-.1817538{col 29}{space 2} .1575699{col 40}{space 1}   -1.15{col 49}{space 3}0.249{col 57}{space 4} -.490585{col 70}{space 3} .1270775
{txt}{space 4}num_motives {c |}{col 17}{res}{space 2} .1564943{col 29}{space 2}  .165515{col 40}{space 1}    0.95{col 49}{space 3}0.344{col 57}{space 4}-.1679091{col 70}{space 3} .4808976
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.361728{col 29}{space 2} .3612623{col 40}{space 1}    6.54{col 49}{space 3}0.000{col 57}{space 4} 1.653667{col 70}{space 3} 3.069789
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}-.2117639{col 29}{space 2} .1861367{col 57}{space 4}-.5765851{col 70}{space 3} .1530573
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} .8091557{col 29}{space 2} .1506136{col 57}{space 4} .5618136{col 70}{space 3} 1.165392
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. nbreg excep_frame strategic_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1095.7543}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1095.6722}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1095.6722}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-472.73555}  
Iteration 1:{space 3}log pseudolikelihood = {res:-464.60443}  
Iteration 2:{space 3}log pseudolikelihood = {res:-464.59767}  
Iteration 3:{space 3}log pseudolikelihood = {res:-464.59767}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -453.6192}  
Iteration 1:{space 3}log pseudolikelihood = {res:-451.58307}  
Iteration 2:{space 3}log pseudolikelihood = {res:-451.36891}  
Iteration 3:{space 3}log pseudolikelihood = {res: -451.3683}  
Iteration 4:{space 3}log pseudolikelihood = {res: -451.3683}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    103.43
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -451.3683{txt}{col 49}Pseudo R2{col 67}= {res}    0.0285

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} excep_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}strategic_o {c |}{col 14}{res}{space 2} .5434908{col 26}{space 2} .1484595{col 37}{space 1}    3.66{col 46}{space 3}0.000{col 54}{space 4} .2525155{col 67}{space 3} .8344661
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2} .3978568{col 26}{space 2} .2087682{col 37}{space 1}    1.91{col 46}{space 3}0.057{col 54}{space 4}-.0113213{col 67}{space 3} .8070349
{txt}{space 4}rightist {c |}{col 14}{res}{space 2} .1942607{col 26}{space 2} .2340686{col 37}{space 1}    0.83{col 46}{space 3}0.407{col 54}{space 4}-.2645054{col 67}{space 3} .6530268
{txt}{space 4}audience {c |}{col 14}{res}{space 2}-.4033095{col 26}{space 2} .2532182{col 37}{space 1}   -1.59{col 46}{space 3}0.111{col 54}{space 4}-.8996081{col 67}{space 3}  .092989
{txt}{space 7}onset {c |}{col 14}{res}{space 2} -.359929{col 26}{space 2} .2194396{col 37}{space 1}   -1.64{col 46}{space 3}0.101{col 54}{space 4}-.7900227{col 67}{space 3} .0701646
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2}-.0091609{col 26}{space 2} .1517046{col 37}{space 1}   -0.06{col 46}{space 3}0.952{col 54}{space 4}-.3064965{col 67}{space 3} .2881746
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  1.62114{col 26}{space 2} .2235409{col 37}{space 1}    7.25{col 46}{space 3}0.000{col 54}{space 4} 1.183008{col 67}{space 3} 2.059273
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2} .2893122{col 26}{space 2} .1532278{col 54}{space 4}-.0110088{col 67}{space 3} .5896331
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} 1.335509{col 26}{space 2}  .204637{col 54}{space 4} .9890516{col 67}{space 3} 1.803327
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. nbreg excep_frame humanitarian_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1161.4046}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1161.4004}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1161.4004}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-472.73555}  
Iteration 1:{space 3}log pseudolikelihood = {res:-464.60443}  
Iteration 2:{space 3}log pseudolikelihood = {res:-464.59767}  
Iteration 3:{space 3}log pseudolikelihood = {res:-464.59767}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-456.91859}  
Iteration 1:{space 3}log pseudolikelihood = {res:-455.73426}  
Iteration 2:{space 3}log pseudolikelihood = {res:-455.66998}  
Iteration 3:{space 3}log pseudolikelihood = {res:-455.66989}  
Iteration 4:{space 3}log pseudolikelihood = {res:-455.66989}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     23.23
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0007
{txt}Log pseudolikelihood = {res}-455.66989{txt}{col 49}Pseudo R2{col 67}= {res}    0.0192

{txt}{ralign 80:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}   excep_frame{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
humanitarian_o {c |}{col 16}{res}{space 2}-.3633352{col 28}{space 2} .1685496{col 39}{space 1}   -2.16{col 48}{space 3}0.031{col 56}{space 4}-.6936864{col 69}{space 3} -.032984
{txt}{space 6}cold_war {c |}{col 16}{res}{space 2} .4925838{col 28}{space 2} .2502788{col 39}{space 1}    1.97{col 48}{space 3}0.049{col 56}{space 4} .0020464{col 69}{space 3} .9831212
{txt}{space 6}rightist {c |}{col 16}{res}{space 2} .2829662{col 28}{space 2} .2291609{col 39}{space 1}    1.23{col 48}{space 3}0.217{col 56}{space 4}-.1661808{col 69}{space 3} .7321133
{txt}{space 6}audience {c |}{col 16}{res}{space 2}-.3392978{col 28}{space 2} .2384247{col 39}{space 1}   -1.42{col 48}{space 3}0.155{col 56}{space 4}-.8066016{col 69}{space 3} .1280059
{txt}{space 9}onset {c |}{col 16}{res}{space 2}-.2654792{col 28}{space 2} .2123806{col 39}{space 1}   -1.25{col 48}{space 3}0.211{col 56}{space 4}-.6817376{col 69}{space 3} .1507792
{txt}{space 3}num_motives {c |}{col 16}{res}{space 2} .0602455{col 28}{space 2}  .225973{col 39}{space 1}    0.27{col 48}{space 3}0.790{col 56}{space 4}-.3826535{col 69}{space 3} .5031445
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.347384{col 28}{space 2} .4255527{col 39}{space 1}    5.52{col 48}{space 3}0.000{col 56}{space 4} 1.513316{col 69}{space 3} 3.181452
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2} .3590426{col 28}{space 2} .1564529{col 56}{space 4} .0524005{col 69}{space 3} .6656847
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} 1.431958{col 28}{space 2}  .224034{col 56}{space 4} 1.053798{col 69}{space 3} 1.945822
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. nbreg excep_frame territorial_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1168.2718}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1168.2717}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-472.73555}  
Iteration 1:{space 3}log pseudolikelihood = {res:-464.60443}  
Iteration 2:{space 3}log pseudolikelihood = {res:-464.59767}  
Iteration 3:{space 3}log pseudolikelihood = {res:-464.59767}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-457.50572}  
Iteration 1:{space 3}log pseudolikelihood = {res:-455.80945}  
Iteration 2:{space 3}log pseudolikelihood = {res:-455.65187}  
Iteration 3:{space 3}log pseudolikelihood = {res:-455.65139}  
Iteration 4:{space 3}log pseudolikelihood = {res:-455.65139}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     25.49
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log pseudolikelihood = {res}-455.65139{txt}{col 49}Pseudo R2{col 67}= {res}    0.0193

{txt}{ralign 79:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}  excep_frame{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
territorial_o {c |}{col 15}{res}{space 2} -.814321{col 27}{space 2} .4400725{col 38}{space 1}   -1.85{col 47}{space 3}0.064{col 55}{space 4}-1.676847{col 68}{space 3} .0482052
{txt}{space 5}cold_war {c |}{col 15}{res}{space 2} .9643739{col 27}{space 2}  .353034{col 38}{space 1}    2.73{col 47}{space 3}0.006{col 55}{space 4}   .27244{col 68}{space 3} 1.656308
{txt}{space 5}rightist {c |}{col 15}{res}{space 2} .0114389{col 27}{space 2}  .344963{col 38}{space 1}    0.03{col 47}{space 3}0.974{col 55}{space 4}-.6646763{col 68}{space 3}  .687554
{txt}{space 5}audience {c |}{col 15}{res}{space 2}-.4905246{col 27}{space 2}  .272724{col 38}{space 1}   -1.80{col 47}{space 3}0.072{col 55}{space 4}-1.025054{col 68}{space 3} .0440047
{txt}{space 8}onset {c |}{col 15}{res}{space 2}-.4083314{col 27}{space 2} .2065532{col 38}{space 1}   -1.98{col 47}{space 3}0.048{col 55}{space 4}-.8131682{col 68}{space 3}-.0034946
{txt}{space 2}num_motives {c |}{col 15}{res}{space 2} .4982689{col 27}{space 2} .3291814{col 38}{space 1}    1.51{col 47}{space 3}0.130{col 55}{space 4}-.1469148{col 68}{space 3} 1.143453
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.574102{col 27}{space 2} .4093121{col 38}{space 1}    3.85{col 47}{space 3}0.000{col 55}{space 4}  .771865{col 68}{space 3} 2.376339
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}/lnalpha {c |}{col 15}{res}{space 2} .3601737{col 27}{space 2} .1737412{col 55}{space 4} .0196472{col 68}{space 3} .7007003
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        alpha {c |}{col 15}{res}{space 2} 1.433578{col 27}{space 2} .2490717{col 55}{space 4} 1.019841{col 68}{space 3} 2.015163
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. nbreg immin_frame strategic_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-754.81623}  
Iteration 1:{space 3}log pseudolikelihood = {res:-754.81613}  
Iteration 2:{space 3}log pseudolikelihood = {res:-754.81613}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-441.37331}  
Iteration 1:{space 3}log pseudolikelihood = {res:-439.47076}  
Iteration 2:{space 3}log pseudolikelihood = {res:-439.46811}  
Iteration 3:{space 3}log pseudolikelihood = {res:-439.46811}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-433.47425}  
Iteration 1:{space 3}log pseudolikelihood = {res:-432.86761}  
Iteration 2:{space 3}log pseudolikelihood = {res:-432.85855}  
Iteration 3:{space 3}log pseudolikelihood = {res:-432.85855}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     13.65
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0338
{txt}Log pseudolikelihood = {res}-432.85855{txt}{col 49}Pseudo R2{col 67}= {res}    0.0150

{txt}{ralign 78:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} immin_frame{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}strategic_o {c |}{col 14}{res}{space 2} .2809826{col 26}{space 2} .1242878{col 37}{space 1}    2.26{col 46}{space 3}0.024{col 54}{space 4} .0373829{col 67}{space 3} .5245823
{txt}{space 4}cold_war {c |}{col 14}{res}{space 2} .0763115{col 26}{space 2}  .155522{col 37}{space 1}    0.49{col 46}{space 3}0.624{col 54}{space 4} -.228506{col 67}{space 3} .3811291
{txt}{space 4}rightist {c |}{col 14}{res}{space 2} .0207788{col 26}{space 2} .2113555{col 37}{space 1}    0.10{col 46}{space 3}0.922{col 54}{space 4}-.3934705{col 67}{space 3}  .435028
{txt}{space 4}audience {c |}{col 14}{res}{space 2} .0370942{col 26}{space 2} .1786476{col 37}{space 1}    0.21{col 46}{space 3}0.836{col 54}{space 4}-.3130487{col 67}{space 3}  .387237
{txt}{space 7}onset {c |}{col 14}{res}{space 2}-.2196152{col 26}{space 2} .1960276{col 37}{space 1}   -1.12{col 46}{space 3}0.263{col 54}{space 4}-.6038222{col 67}{space 3} .1645918
{txt}{space 1}num_motives {c |}{col 14}{res}{space 2}-.1722825{col 26}{space 2} .1370398{col 37}{space 1}   -1.26{col 46}{space 3}0.209{col 54}{space 4}-.4408756{col 67}{space 3} .0963105
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.136266{col 26}{space 2} .2396122{col 37}{space 1}    8.92{col 46}{space 3}0.000{col 54}{space 4} 1.666635{col 67}{space 3} 2.605898
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-.3708907{col 26}{space 2} .2215955{col 54}{space 4}-.8052099{col 67}{space 3} .0634286
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       alpha {c |}{col 14}{res}{space 2} .6901194{col 26}{space 2} .1529274{col 54}{space 4} .4469941{col 67}{space 3} 1.065483
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. nbreg immin_frame counterterror_o $c, dispersion(mean) vce(cluster leader)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -777.0857}  
Iteration 1:{space 3}log pseudolikelihood = {res:-777.08481}  
Iteration 2:{space 3}log pseudolikelihood = {res:-777.08481}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-441.37331}  
Iteration 1:{space 3}log pseudolikelihood = {res:-439.47076}  
Iteration 2:{space 3}log pseudolikelihood = {res:-439.46811}  
Iteration 3:{space 3}log pseudolikelihood = {res:-439.46811}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-436.14159}  
Iteration 1:{space 3}log pseudolikelihood = {res: -435.8975}  
Iteration 2:{space 3}log pseudolikelihood = {res:-435.89597}  
Iteration 3:{space 3}log pseudolikelihood = {res:-435.89597}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       136
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     20.32
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0024
{txt}Log pseudolikelihood = {res}-435.89597{txt}{col 49}Pseudo R2{col 67}= {res}    0.0081

{txt}{ralign 81:(Std. Err. adjusted for {res:12} clusters in leader)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}    immin_frame{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
counterterror_o {c |}{col 17}{res}{space 2}-.2034616{col 29}{space 2} .0695085{col 40}{space 1}   -2.93{col 49}{space 3}0.003{col 57}{space 4}-.3396958{col 70}{space 3}-.0672275
{txt}{space 7}cold_war {c |}{col 17}{res}{space 2} .1730159{col 29}{space 2} .2082755{col 40}{space 1}    0.83{col 49}{space 3}0.406{col 57}{space 4}-.2351966{col 70}{space 3} .5812284
{txt}{space 7}rightist {c |}{col 17}{res}{space 2} .0827681{col 29}{space 2} .2017053{col 40}{space 1}    0.41{col 49}{space 3}0.682{col 57}{space 4}-.3125671{col 70}{space 3} .4781033
{txt}{space 7}audience {c |}{col 17}{res}{space 2} .0625834{col 29}{space 2} .1803143{col 40}{space 1}    0.35{col 49}{space 3}0.729{col 57}{space 4} -.290826{col 70}{space 3} .4159929
{txt}{space 10}onset {c |}{col 17}{res}{space 2}-.2695627{col 29}{space 2} .2084264{col 40}{space 1}   -1.29{col 49}{space 3}0.196{col 57}{space 4}-.6780711{col 70}{space 3} .1389456
{txt}{space 4}num_motives {c |}{col 17}{res}{space 2}-.1018004{col 29}{space 2} .1754885{col 40}{space 1}   -0.58{col 49}{space 3}0.562{col 57}{space 4}-.4457517{col 70}{space 3} .2421508
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.405532{col 29}{space 2} .3138077{col 40}{space 1}    7.67{col 49}{space 3}0.000{col 57}{space 4}  1.79048{col 70}{space 3} 3.020583
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}-.3218527{col 29}{space 2} .2261011{col 57}{space 4}-.7650027{col 70}{space 3} .1212973
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} .7248049{col 29}{space 2} .1638792{col 57}{space 4} .4653327{col 70}{space 3}  1.12896
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Kerry\Documents\Alchemy\Publications\230915 - framil\Publication\Replication files\framil_chavez_replicationlog.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}19 Sep 2023, 17:08:38
{txt}{.-}
{smcl}
{txt}{sf}{ul off}